Data Cube Implementations

Contents

Implementer's note: The WG is considering a change to the normalization algorithm to improve the coverage of the integrity checking rules. Please see [1] for details and respond if this would cause problems.

A summary of known implementations of Data Cube is given below, followed by a table of conformance results that have been formally reported.

Data Cube implementations

Reporter

Description

Link (if public)

Dave Reynolds

Environment Agency, Bathing water quality. Data Cubes are used to represent but current and history weekly and annual assessments of quality of water at bathing locations in England and Wales

Local government payments. A number of UK Local Authorities have published Linked Data describing payments made to suppliers. This was achieved via the payments ontology, an extension of the Data Cube vocabulary.

SEC Edgar Linked Data Wrapper. Data Cubes are used to represent XBRL data from the U.S. Securities and Exchange Commission. For example, those cubes are used in the Financial Information Observation System (FIOS) [10].

ISTAT Immigration. This dataset collects official statistical data about immigration in Italy, provided by the Italian National Institute of Statistics (dati.istat.it). Data is represented by means of the Data Cube vocabulary.

Computex. This implementation is called Computex (Computational Statistical Indexes). It can be seen as an extension of RDF Data Cube to represent statistical indexes that can be automatically computed using SPARQL queries.

Linked Data Cubes Explorer (LDCX): LDCX takes in the URI of a dataset, loads it from Linked Data, executes the normalisation algorithm and integrity checks, and allows users to create pivot tables from the dataset. As such, LDCX may be useful to two groups: 1) Users of statistical Linked Data that want to explore a dataset and 2) Publishers of statistical Linked Data that want to validate their publication.

European Environment Agency: is using the Data Cube Vocabulary to be able to import SDMX datasets from other organisations into our triple store for further work. This work typically involves combining two or more datasets. Activity includes data conversion and visualization.

Digital Scoreboard is a collection of over 100 selected indicators which illustrate some key dimensions of the European information society and allow a comparison of progress across European countries as well as over time.

IC-1, IC-3 and IC-4 fail correctly. None of these failures reflect errors in the rules but limitations of the early stage data (IC-1, IC-4) and lack of normalization in the validation process used (IC-3). [38]

IC-4 fails correctly [40]. The implementation report explains that the data is automatically generated from SDMX-ML to RDF transformation which is not yet able to infer the range statements needed for IC4.